Cutting inference cold starts by 40x with LP, FUSE, C/R, and CUDA-checkpoint
Key takeaways
- Billion- to trillion-parameter neural networks are run on specialized accelerators at quadrillions of operations per second to generate media, author software, and fold proteins at massive scale.
- Inference workloads are more variable and less predictable than the training workloads that previously dominated.
- But serverless computing only works if new replicas can be spun up quickly — as fast as demand changes, which can be at the scale of seconds.
All posts Back Engineering May 12, 2026•20 minute read Cutting inference cold starts by 40x with LP, FUSE, C/R, and cuda-checkpoint Charles Frye@charles_irl Member of Technical Staff Jonathan Belotti@jonobelotti_IO Member of Technical Staff Erik Bernhardsson@bernhardsson CEO and Founder Akshat Bubna@akshat_b CTO and Founder
We are in the age of inference. Billion- to trillion-parameter neural networks are run on specialized accelerators at quadrillions of operations per second to generate media, author software, and fold proteins at massive scale.
Inference workloads are more variable and less predictable than the training workloads that previously dominated. That makes them a natural fit for serverless computing, where applications are defined at a level above the (virtual) machine so that they can be more readily scaled up and down to handle variable load.